Inefficiencies in Zonal Market Coupling due to Uncertainties in Generation Shift Keys

Transcription

1 Inefficiencies in Zonal Market Coupling due to Uncertainties in Generation Shift Keys Björn Felten, Tim Felling, Christoph Weber Chair for Management Science and Energy Economics University of Duisburg-Essen 40th IAEE International Conference Singapore,

2 Agenda Introduction Theoretical Background Application Results & Conclusions Concurrent Session 18 2

3 Agenda Introduction Theoretical Background Application Results & Conclusions Concurrent Session 18 3

4 Overview Market Coupling Process in Central Western Europe (CWE) Two days in advance D-2 One day in advance D-1 Time of Delivery D Redispatch possible D-7 D-6D-5 Price zone A Price D-4 zone B D-3 Price zone C TSOs calculate parameters for Flow- Based market coupling algorithm Market clearing (incl. market coupling computation) Matching bids & asks with aim of welfare maximization Dispatch, prices, net exchanges Delivery of electricity (Redispatch measures if necessary to prevent critical grid situations) Concurrent Session 18 4 Sources: BNetzA, BKartA (2015), Monitoringbericht 2015; EPEX Spot et al. (2016), Euphemia Public Description

5 Overview Market Coupling Process in Central Western Europe (CWE) Two days in advance D-2 One day in advance D-1 Time of Delivery D Redispatch possible D-7 D-6D-5 D-4 D-3 Price zone A Price zone B Price zone C TSOs calculate parameters for Flow- Based market coupling algorithm Market clearing (incl. market coupling computation) Matching bids & asks with aim of welfare maximization Dispatch, prices, net exchanges Delivery of electricity (Redispatch measures if necessary to prevent critical grid situations) Uncertainty of line flows due to infeed & load assumption Concurrent Session 18 5 Sources: BNetzA, BKartA (2015), Monitoringbericht 2015; EPEX Spot et al. (2016), Euphemia Public Description

6 Overview Market Coupling Process in Central Western Europe (CWE) Two days in advance D-2 One day in advance D-1 Time of Delivery D Redispatch possible D-7 D-6D-5 D-4 D-3 Price zone A Price zone B Price zone C TSOs calculate parameters for Flow- Based market coupling algorithm Market clearing (incl. market coupling computation) Matching bids & asks with aim of welfare maximization Dispatch, prices, net exchanges Delivery of electricity (Redispatch measures if necessary to prevent critical grid situations) Uncertainty of line flows due to infeed & load assumption Include security margins in the parameters = Flow Reliability Margins (FRMs) Decrease effects of uncertainty Concurrent Session 18 6 Sources: BNetzA, BKartA (2015), Monitoringbericht 2015; EPEX Spot et al. (2016), Euphemia Public Description

7 Overview Market Coupling Process in Central Western Europe (CWE) Two days in advance D-2 One day in advance D-1 Time of Delivery D Redispatch possible D-7 D-6D-5 D-4 D-3 Price zone A Price zone B Price zone C TSOs calculate parameters for Flow- Based market coupling algorithm Main Research Questions: Uncertainty of line flows due to infeed & load assumption Include security margins in the = parameters Market clearing (incl. market coupling computation) Matching bids & asks with aim of welfare maximization Dispatch, prices, net exchanges How to assess the Flow Reliability Margins (FRMs)? Flow Reliability Margins (FRMs) Delivery of electricity (Redispatch measures if necessary to prevent critical grid situations) How high do the FRMs need to be in order to prevent redispatch? How do alternative Market Designs influence the FRMs? Decrease effects of uncertainty Concurrent Session 18 7 Sources: BNetzA, BKartA (2015), Monitoringbericht 2015; EPEX Spot et al. (2016), Euphemia Public Description

8 Agenda Introduction Theoretical Background Application Results & Conclusions Concurrent Session 18 8

9 4 nodes example - introduction Nodal system Zonal system i c g node index variable costs generation f cap A(i,f) ISO line index (line) capacity nodal power transfer distribution factor Independent System Operator q d A(z,f) CWE net export demand zonal power transfer distribution factor Central Western Europe GSK g max,i X Generation Shift Key nodal generation capacity margin to account for uncertainty Concurrent Session 18 9

10 4 nodes example - commonalities Nodal system Zonal system well-functioning competition: market outcome = outcome of a central optimization i c g node index variable costs generation f cap A(i,f) ISO line index (line) capacity nodal power transfer distribution factor Independent System Operator q d A(z,f) CWE net export demand zonal power transfer distribution factor Central Western Europe GSK g max,i X Generation Shift Key nodal generation capacity margin to account for uncertainty Concurrent Session 18 10

11 4 nodes example - differences Nodal system Zonal system well-functioning competition: market outcome = outcome of a central optimization ISO: Integrated planning (generation and grid) Less uncertainty due to grid CWE: D-2 expectation used for D-1 i c g node index variable costs generation f cap A(i,f) ISO line index (line) capacity nodal power transfer distribution factor Independent System Operator q d A(z,f) CWE net export demand zonal power transfer distribution factor Central Western Europe GSK g max,i X Generation Shift Key nodal generation capacity margin to account for uncertainty Concurrent Session 18 11

12 4 node example perfect foresight Perfect Foresight = 100 % Certainty (GSK r = GSK e ) Solution space results from convex hull (polyhedron) of zonal line constraints Concurrent Session 18 12

13 4 node example one-sided uncertainty Uncertainty: Potentially realized GSK (r) GSK e Line loads can differ from expectation Solution space is smaller (if redispatch is to be prevented) Concurrent Session 18 13

14 4 node example two-sided uncertainty Uncertainties in both directions make it more complicated Concurrent Session 18 14

15 4 nodes example critical situations Critical exchange situations are the corners of the convex hull of the potentially realized line constraints (family of hyper planes) Concurrent Session 18 15

16 4 nodes example ex. critical situation Let s pick an example Prevention of potential redispatch Concurrent Session 18 16

17 4 nodes example FRM Prevention of potential redispatch original zonal line capacity constraint required zonal line capacity constraint in worst case FRM = Flow Reliability Margin Concurrent Session 18 17

18 4 nodes example FRM Prevention of potential redispatch Potential welfare reduction original zonal line capacity constraint required zonal line capacity constraint in worst case FRM = Flow Reliability Margin Concurrent Session 18 18

19 4 nodes example FRM How to determine the FRMs required to prevent redispatch? How do FRMs develop with diminishing price zone size? original zonal line capacity constraint required zonal line capacity constraint in worst case FRM = Flow Reliability Margin Concurrent Session 18 19

20 Derivation of worst case FRMs Linear supply function GSK formulation Derivative of zonal PTDF Taylor approximation of load flow (1st order) Formulating linear optimization problem s.t. Concurrent Session 18 20

21 Agenda Introduction Theoretical Background Application Results & Conclusions Concurrent Session 18 21

22 Application Used Case Current CWE+ Methodology from: Felling, Weber (2016), Identifying price zones using Nodal prices and supply & demand weighted nodes Concurrent Session 18 22

23 Application - Algorithm Determine Price Zones Determine nodal PTDFs Calculate zonal PTDFs Matpower: R. D. Zimmermann, C. E. Murillo-Sanchez, and R. J. Thomas, Matpower: Steady-State Operations, Planning and Analysis Tools for Power Systems Research and Education", IEEE Transactions on Power Systems, vol. 26, no. 1, pp , Feb Operational Handbook Entso-E: Solve zonal market clearing (for representative exchange situations) As explained in the 4 nodes example Solve maximization problem for FRMs As shown before Concurrent Session 18 23

24 Agenda Introduction Theoretical Background Application Results & Conclusions Concurrent Session 18 24

25 First results relative worst-case FRMs preliminary Concurrent Session 18 25

26 Conclusions Methods: A theoretical deviation of Worst-Case Flow Reliability Margins (FRMs) has been accomplished A practical methodology for estimating FRMs in real-world systems has been proposed and tested Observations: FRMs tend to decrease with increasing number of price zones However the decrease is not monotonic (i.e. eventually adding one zone may increase the Worst-Case FRM slightly) Particularities of the grid, the price zone configurations and the considered exchange situations effect the Worst-Case FRMs (e.g. country 5 zones) Next steps: Further analyses (e.g.: driving factors for uncertainty impact, more restrictions, interdependencies of FRM choices) Concurrent Session 18 26

27 Thank you for your attention Authors: Björn Felten > Tim Felling > Christoph Weber > Concurrent Session 18 27

28 Backup Concurrent Session 18 28

29 Backup Average FRM contribution of node within zone Concurrent Session 18 29

30 Backup further FRMs preliminary Concurrent Session 18 30